{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.652330Z",
     "start_time": "2025-11-03T08:42:04.647807Z"
    }
   },
   "source": "import pandas as pd",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.699877Z",
     "start_time": "2025-11-03T08:42:04.681231Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#读取数据\n",
    "advertising = pd.read_csv('../../data/advertising.csv')\n",
    "print(advertising.head())\n",
    "print(advertising.describe())\n",
    "print(advertising.shape)"
   ],
   "id": "5612b3574a6ed939",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0     TV  Radio  Newspaper  Sales\n",
      "0           1  230.1   37.8       69.2   22.1\n",
      "1           2   44.5   39.3       45.1   10.4\n",
      "2           3   17.2   45.9       69.3    9.3\n",
      "3           4  151.5   41.3       58.5   18.5\n",
      "4           5  180.8   10.8       58.4   12.9\n",
      "       Unnamed: 0          TV       Radio   Newspaper       Sales\n",
      "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
      "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
      "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
      "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
      "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
      "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
      "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
      "max    200.000000  296.400000   49.600000  114.000000   27.000000\n",
      "(200, 5)\n"
     ]
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.730971Z",
     "start_time": "2025-11-03T08:42:04.722019Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据预处理\n",
    "# 去掉第一列\n",
    "advertising.drop(advertising.columns[0], axis=1, inplace=True)\n",
    "# 去掉空值操作\n",
    "advertising.dropna(inplace=True)\n",
    "# 提取特征和标签\n",
    "X = advertising.drop(\"Sales\", axis=1)\n",
    "y = advertising[\"Sales\"]\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ],
   "id": "5bb2c8f4c6bb6935",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 3)\n",
      "(200,)\n"
     ]
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.762368Z",
     "start_time": "2025-11-03T08:42:04.755970Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 计算皮尔逊相关系数\n",
    "print(X.corrwith(y, method='pearson'))"
   ],
   "id": "7a1a8728a56954c3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TV           0.782224\n",
      "Radio        0.576223\n",
      "Newspaper    0.228299\n",
      "dtype: float64\n"
     ]
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.831227Z",
     "start_time": "2025-11-03T08:42:04.819758Z"
    }
   },
   "cell_type": "code",
   "source": [
    "corr_matrix = advertising.corr(method='pearson')\n",
    "print(corr_matrix)"
   ],
   "id": "72df703553066f27",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 TV     Radio  Newspaper     Sales\n",
      "TV         1.000000  0.054809   0.056648  0.782224\n",
      "Radio      0.054809  1.000000   0.354104  0.576223\n",
      "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
      "Sales      0.782224  0.576223   0.228299  1.000000\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:04.894785Z",
     "start_time": "2025-11-03T08:42:04.889421Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将相关热力矩阵绘制成热力图\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ],
   "id": "655932c4beb9f7e6",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-03T08:42:05.117947Z",
     "start_time": "2025-11-03T08:42:04.932024Z"
    }
   },
   "cell_type": "code",
   "source": [
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='0.2f')\n",
    "plt.title(\"Pearson Feature Correlation Matrix\")\n",
    "plt.show()"
   ],
   "id": "d79d294d9eef38f1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 38
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
